Various types of signals are processed. In voice signals, amplitude values are typically a function of time. In image signals, pixel intensity values are typically a function of (x,y) coordinates in an image. Such signals can be processed to distinguish one or more objects represented in the signals. For example, amplitude values in voice signals can be processed to distinguish objects such as spoken words represented in the voice signals. Similarly, pixel intensity values in image signals can be processed to distinguish one or more target objects represented in the image.
Such objects can be distinguished by processing the signals to acquire various types of measurements. The acquired measurements are evaluated relative to known measurements ("features") of objects in order to distinguish an object as being either represented or not represented in the signals. In evaluating acquired measurements, efficiency is desirably increased by selecting the acquired measurements for evaluation in an optimal order, so that uncertainty regarding the object's representation is eliminated in a short amount of processing time.
According to many previous techniques, a human developer manually selects measurements for evaluation, relying upon the human developer's subjective intuition concerning the manner in which acquired measurements relate to the goal of distinguishing objects. Nevertheless, such introspective techniques might have poor effectiveness if acquired measurements are difficult to relate to the goal of distinguishing the object. For example, it is difficult to intuitively relate the raw signal output from a naval inverse synthetic aperture radar ("ISAR") to target objects for recognition.
In some previous techniques, predefined types of measurements are always evaluated. A shortcoming of such techniques is that they fail to adapt to a recognition domain by selecting measurements for evaluation according to each measurement's usefulness. A recognition domain includes the signal acquisition device, signal environment, available measurement types, and objects to be distinguished. Essentially, such previous techniques have relatively poor efficiency and poor robustness, because they evaluate measurements according to a preconceived recognition strategy rather than by adapting to the recognition domain. Moreover, such previous techniques are frequently ineffective at distinguishing among a large number of different objects.
Certain previous techniques use a binary decision tree having one or more nodes each specifying an associated measurement for evaluation. Each node has only two branches separated by a single associated threshold value. As the binary decision tree is traversed, one of a node's two branches is specified in response to an evaluated measurement either satisfying or failing to satisfy the single threshold value. Often, it is desirable to represent more than two values for a measurement. In this case, the binary decision tree uses more than one node to represent the measurement's value. For example, a measurement for which it is desirable to represent three values would require two binary nodes.
By requiring more than one node to represent such a measurement's value, binary decision trees inefficiently occupy additional system memory, and additional processing time is consumed in traversing the binary decision tree. Accordingly, binary decision trees fail to select a suitable number of branches by adapting to a recognition domain. Likewise, binary decision trees fail to select suitable threshold values associated with the suitably selected number of branches by adapting to the recognition domain.
In response to available measurements, it might be impractical to distinguish objects in an absolutely reliable manner. In such cases, some previous techniques fail to provide a sufficiently accurate estimate of their reliability in distinguishing objects in response to available measurements.
Also, some previous techniques fail to provide a sufficiently accurate estimate of a measurement's probability of having a value within a specified range.
Thus, a need has arisen for a method and system for distinguishing among a large number of different objects in an efficient and robust manner. A need has also arisen for a method and system for distinguishing objects in a short amount of processing time. A further need has arisen for a method and system for distinguishing objects, in which system memory is efficiently occupied. Moreover, a need has arisen for a method and system for distinguishing objects by adapting to a recognition domain. Another need has arisen for a method and system for distinguishing objects, in which a sufficiently accurate estimate is provided of reliability in distinguishing objects in response to available measurements.
Also, a need has arisen for a method and system for distinguishing objects, in which measurements are objectively selected for evaluation in an optimal order. Further, a need has arisen for a method and system for distinguishing objects, in which measurements are selected for evaluation according to each measurement's usefulness by adapting to a recognition domain. An even further need has arisen for a method and system for distinguishing objects, in which measurements are effectively selected for evaluation even if introspection is difficult.
Additionally, a need has arisen for a method and system for distinguishing objects, in which more than two possible measurement values are represented in an efficient manner. Yet a further need has arisen for a method and system for distinguishing objects, in which a suitable number of branches is selected for a node of a decision tree, and in which one or more suitable threshold values associated with the suitably selected number of branches are selected by adapting to a recognition domain.
Finally, a need has arisen for a method and system for providing a sufficiently accurate estimate of a measurement's probability of having a value within a specified range.